[en] [en] BACKGROUND: Positron Emission Tomography (PET) is a powerful diagnostic tool, but its availability, high cost and radiation burden limit its accessibility. Deep learning-based denoising offers a potential solution by enabling low-count PET scans, reducing tracer dose or scan time without compromising diagnostic utility. However, clinical validation of such approaches across different scanner technologies and radiotracers remains limited.
METHODS: We conducted a multicenter, blinded evaluation of NUCLARITY, a deep learning-based denoising software, using PET data from three European hospitals. Data included 65 scans acquired with [1⁸F]FDG, [1⁸F]PSMA, [68 Ga]PSMA, and [68 Ga]DOTATATE on GE and Siemens systems not seen during model training. Low-count scans (50% simulated) were denoised and compared to full-count clinical scans. Image quality was assessed using RMSE, PSNR, and SSIM. Six nuclear physicians evaluated diagnostic image quality (DIQ), diagnostic confidence (DC), and lesion detection across six anatomical regions. Lesion quantification was compared using SUVmean, SUVmax, and MTV.
RESULTS: Low-count enhanced (LCE) scans showed improved quantitative image quality metrics compared to unenhanced low-count scans (higher PSNR/SSIM, lower RMSE). Across 243 lesions, SUVmean and SUVmax showed high concordance between standard-count (SC) and LCE scans (CCC = 1.00 and 0.99, respectively). Diagnostic image quality and confidence were slightly lower on LCE versus SC scans, but only one reader indicated a clear preference for SC. Sensitivity and specificity for lesion detection in LCE scans were 99% and 99%, respectively, with interscan agreement exceeding inter-reader variability.
CONCLUSIONS: This is the first blinded, multicenter reader study evaluating a PET denoising algorithm in a European clinical setting across multiple tracers, incorporating unseen scanner technologies. The denoising algorithm demonstrated robust generalizability and preserved diagnostic accuracy on 50% count data. These findings support the clinical adoption of deep learning-based PET denoising to reduce dose or scan time for four commonly used tracers.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Maes, Justine ✱; Division of Nuclear Medicine, University Hospitals UZ Leuven, Louvain, Belgium
Carron, Charles ✱; Division of Nuclear Medicine, University Hospitals UZ Leuven, Louvain, Belgium
DeKeyser, Simon ✱; Nuclivision, Ghent, Belgium
Brants, Tomas; Nuclivision, Ghent, Belgium
De Ridder, Vicky; Nuclivision, Ghent, Belgium
Chaouki, Amine; Department of Nuclear Medicine, Chirec Hospital Group, Brussels, Belgium
Geraldes CFGC Introduction to infrared and raman-based biomedical molecular imaging and comparison with other modalities Molecules 2020 10.3390/molecules25235547 33256052 7731440
Li W Fang L Li J Exposure doses to technologists working in 7 PET/CT departments’ Dose Response 2020 18 3 10.1177/1559325820938288 32694961 7350403 1559325820938288
McCann A Vintró LL Cournane S Lucey J Assessment of occupational exposure from shielded and unshielded syringes for clinically relevant positron emission tomography (PET) isotopes-a Monte Carlo approach using EGSnrc J Radiol Prot 2021 10.1088/1361-6498/ac0df5 34161938
Farkas J Martin M Nielsen C Jennings SG The effects on technologist occupational exposure in PET/CT departments when working with students at various levels of supervision J Nucl Med Technol 2020 48 3 214 7 10.2967/jnmt.119.241398 32312851
Wen JC Sai V Straatsma BR McCannel TA Radiation-related cancer risk associated with surveillance imaging for metastasis from choroidal melanoma JAMA Ophthalmol 2013 131 1 56 61 10.1001/JAMAOPHTHALMOL.2013.564 23307209
Kessara A Buyukcizmeci N Gedik GK Cancer risk estimation for patients undergoing whole-body PET/CT scans Radiat Prot Dosimetry 2023 199 6 509 18 1:CAS:528:DC%2BB3sXhvFKgu7rN 10.1093/RPD/NCAD040 36856709
Subramanian K et al. Complex implementation factors demonstrated when evaluating cost-effectiveness and monitoring racial disparities associated with [18F] DCFPyL PET/CT in prostate cancer men Sci Rep 2023 13 1 1:CAS:528:DC%2BB3sXhtV2is77K 10.1038/s41598-023-35567-w 37221397 10205741 8321
Mason C Gimblet GR Lapi SE Lewis JS Novel tracers and radionuclides in PET imaging Radiol Clin North Am 2021 59 5 887 10.1016/J.RCL.2021.05.012 34392925 9116257
Hashimoto F Onishi Y Ote K Tashima H Reader AJ Yamaya T Deep learning-based PET image denoising and reconstruction: a review Radiol Phys Technol 2024 17 1 24 46 10.1007/s12194-024-00780-3 38319563 10902118
Balaji V Song TA Malekzadeh M Heidari P Dutta J Artificial intelligence for PET and SPECT image enhancement J Nucl Med 2024 65 1 4 12 1:CAS:528:DC%2BB2cXhvFShur8%3D 10.2967/jnumed.122.265000 37945384 10755520
Bousse A et al. A review on low-dose emission tomography post-reconstruction denoising with neural network approaches IEEE Trans Radiat Plasma Med Sci 2024 8 4 333 47 10.1109/TRPMS.2023.3349194 39429805 11486494
Seith F et al. Simulation of tracer dose reduction in 18F-FDG PET/MRI: effects on oncologic reading, image quality, and artifacts J Nucl Med 2017 58 10 1699 1705 1:CAS:528:DC%2BC1cXitVCqu7vE 10.2967/jnumed.116.184440 28360205
Gatidis S Seith F Schäfer JF Christian la Fougère MD Nikolaou K Schwenzer NF Towards tracer dose reduction in PET studies: simulation of dose reduction by retrospective randomized undersampling of list-mode data Hell J Nucl Med 2016 19 1 15 18 26929936
Schaefferkoetter J et al. Convolutional neural networks for improving image quality with noisy PET data EJNMMI Res 2020 10 1 10.1186/S13550-020-00695-1 32955669 7505915 105
Xu J, Gong E, Pauly J, Zaharchuk G. ‘200x low-dose PET reconstruction using deep learning’. arXiv preprint arXiv:1712.04119, 2017.
Jang SI et al. Spach transformer: spatial and channel-wise transformer based on local and global self-attentions for PET image denoising IEEE Trans Med Imaging 2023 43 6 2036 2049 10.1109/TMI.2023.3336237
Pan S et al. Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model Med Phys 2024 51 8 5468 5478 1:CAS:528:DC%2BB2cXhtVygtr7N 10.1002/MP.17068 38588512
Liu Z, et al. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE International Conference on Computer Vision. 2021. pp. 9992–10002. https://doi.org/10.1109/ICCV48922.2021.00986.
Wang YR et al. Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models Eur J Nucl Med Mol Imaging 2023 50 5 1337 50 10.1007/S00259-022-06097-W 36633614 10387227
Xie H, et al. Dose-aware diffusion model for 3D low-dose PET: multi-institutional validation with reader study and real low-dose data. arXiv preprint arXiv:2405.12996, 2024.
Yu B et al. Robust whole-body PET image denoising using 3D diffusion models: evaluation across various scanners, tracers, and dose levels Eur J Nucl Med Mol Imaging 2025 10.1007/S00259-025-07122-4 41137928 12491104
Liu Q et al. A personalized deep learning denoising strategy for low-count PET images Phys Med Biol 2022 67 14 10.1088/1361-6560/ac783d 145014
Rogasch JMM Hofheinz F van Heek L Voltin C-A Boellaard R Kobe C Influences on PET quantification and interpretation Diagnostics 2022 12 2 1:CAS:528:DC%2BB38XptVGkt78%3D 10.3390/diagnostics12020451 35204542 8871060 451
Bonardel G et al. Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition EJNMMI Phys 2022 9 1 10.1186/s40658-022-00465-z 35543894 9095795 36
Quak E Weyts K Jaudet C Prigent A Foucras G Lasnon C Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime Front Med 2023 10.3389/FMED.2023.1137514
Weyts K et al. Artificial intelligence-based PET denoising could allow a two-fold reduction in [18F]FDG PET acquisition time in digital PET/CT Eur J Nucl Med Mol Imaging 2022 49 11 3750 60 1:CAS:528:DC%2BB38XhsVOlsLfK 10.1007/S00259-022-05800-1 35593925 9399218
Mehranian A et al. Deep learning–based time-of-flight (ToF) image enhancement of non-ToF PET scans Eur J Nucl Med Mol Imaging 2022 49 11 3740 3749 1:CAS:528:DC%2BB38XhsVOlsL7I 10.1007/S00259-022-05824-7/FIGURES/6 35507059 9399038
Chaudhari AS et al. Low-count whole-body PET with deep learning in a multicenter and externally validated study NPJ Digit Med 2021 4 1 10.1038/s41746-021-00497-2 34426629 8382711 127
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2015. pp. 770–778. https://doi.org/10.1109/CVPR.2016.90.
Singh G Mittal A Aggarwal N ResDNN: deep residual learning for natural image denoising IET Image Process 2020 14 11 2425 34 10.1049/iet-ipr.2019.0623
Katsari K et al. Artificial intelligence for reduced dose 18F-FDG PET examinations: a real-world deployment through a standardized framework and business case assessment EJNMMI Phys 2021 8 1 10.1186/S40658-021-00374-7 33687602 7943690 25
Dadgar M Verstraete A Maebe J D’Asseler Y Vandenberghe S Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT EJNMMI Phys 2024 11 1 10.1186/s40658-024-00688-2 39412633 11484998 86
